Senior Quantitative Developer at Swish Analytics

Company Description Swish Analytics is a sports analytics, betting, and fantasy startup building the next generation of predictive sports analytics data products. We believe that oddsmaking is a challenge rooted in engineering, mathematics, and sports betting expertise; not intuition.  We're looking for team-oriented individuals with an authentic passion for accurate and predictive real-time data who can execute in a fast-paced, creative, and continually-evolving environment without sacrificing technical excellence. Our challenges are unique, so we hope you are comfortable in uncharted territory and passionate about building systems to support products across a variety of industries and consumer/enterprise clients. Role Overview You'll architect and build the core trading systems that execute our fair value models across sports betting exchanges at scale. This is a systems engineering role focused on real-time decision-making, multi-venue orchestration, and low-latency execution under production constraints. Core Responsibilities Real-Time Trading Engine Architecture • Design event-driven trading systems that consume fair value models and market data to make sub-second execution decisions • Build the core logic for comparing fair values against live market prices and determining when/where to trade • Implement asynchronous order generation, submission, and cancellation workflows across multiple venues with different latency profiles • Design state machines for order lifecycle management (pending, accepted, filled, cancelled, rejected) with proper event ordering and idempotency Multi-Venue Execution & Routing • Build venue-specific integrations (WebSocket connections to Matchbook, Kalshi; REST API adapters for Betfair; FIX protocol handlers) • Implement intelligent order routing that selects optimal venues based on liquidity, fees, latency, and position constraints • Design coordination logic for managing orders across multiple venues when a single bet spans several platforms • Handle venue-specific quirks (rate limiting, connection drops, partial fills, odds movement during submission) Position & Risk Management Systems • Build real-time position tracking systems that aggregate exposure across all venues, markets, and event types • Implement global liability management that enforces risk limits while maximizing capital utilization • Design systems that detect and respond to position drift (when actual fills deviate from intended exposure) • Create reconciliation engines that validate positions against venue reports and detect/resolve discrepancies Data & Execution Infrastructure • Design data pipelines that ingest real-time market data from multiple feeds (WebSocket streams, REST polling, custom adapters) into low-latency in-memory stores • Build efficient order book representation and query systems optimized for fast fair value lookups • Implement message ordering and deduplication logic for ensuring consistent state across async operations • Design persistent logging and event sourcing for order/trade auditing and post-incident analysis Required Qualifications Domain Experience • 3+ years building production trading/market-making systems for betting syndicates, sharp groups, or exchanges • Deep understanding of exchange vs. bookmaker dynamics and practical experience executing against both • Hands-on experience integrating with real-time sports betting data feeds and exchange APIs Technical Fundamentals • 3+ years of production Python with expert-level async/await, event loop, and concurrent execution skills • Strong system design for distributed, real-time, event-driven systems • Deep understanding of database transactions, consistency models, and state management under high throughput • Experience with message streaming platforms (Kafka or equivalent) for order/execution event handling • Proficiency with containerization (Docker), orchestration (Kubernetes), and cloud infrastructure (AWS, GCP) Core Competencies • Ability to architect systems that make correct decisions under tight latency constraints • Strong debugging skills for timing issues, race conditions, and event ordering problems • Systematic problem-solving for production incidents in trading systems • Pragmatic engineering decisions (when to accept latency vs. consistency tradeoffs) Strongly Preferred • Experience building order management systems (OMS) or execution management systems (EMS) • Background in low-latency or high-frequency trading system design • Hands-on work with WebSocket real-time connections and connection resilience patterns • Experience with FIX protocol or similar financial messaging standards • Knowledge of multi-leg execution and cross-product coordination challenges • Familiarity with market microstructure (order book dynamics, market impact, slippage models) • ...